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Crowdsourcing the character of a place : Character‐level convolutional networks for multilingual geographic text classification / Benjamin Adams in Transactions in GIS, vol 22 n° 2 (April 2018)
[article]
Titre : Crowdsourcing the character of a place : Character‐level convolutional networks for multilingual geographic text classification Type de document : Article/Communication Auteurs : Benjamin Adams, Auteur ; Grant McKenzie, Auteur Année de publication : 2018 Article en page(s) : pp 394 - 408 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Toponymie
[Termes IGN] classification
[Termes IGN] contenu généré par les utilisateurs
[Termes IGN] données localisées des bénévoles
[Termes IGN] exploration de texte
[Termes IGN] géocodage
[Termes IGN] méthode robuste
[Termes IGN] réseau neuronal convolutif
[Termes IGN] toponyme
[Termes IGN] traitement du langage naturelRésumé : (Auteur) This article presents a new character‐level convolutional neural network model that can classify multilingual text written using any character set that can be encoded with UTF‐8, a standard and widely used 8‐bit character encoding. For geographic classification of text, we demonstrate that this approach is competitive with state‐of‐the‐art word‐based text classification methods. The model was tested on four crowdsourced data sets made up of Wikipedia articles, online travel blogs, Geonames toponyms, and Twitter posts. Unlike word‐based methods, which require data cleaning and pre‐processing, the proposed model works for any language without modification and with classification accuracy comparable to existing methods. Using a synthetic data set with introduced character‐level errors, we show it is more robust to noise than word‐level classification algorithms. The results indicate that UTF‐8 character‐level convolutional neural networks are a promising technique for georeferencing noisy text, such as found in colloquial social media posts and texts scanned with optical character recognition. However, word‐based methods currently require less computation time to train, so currently are preferable for classifying well‐formatted and cleaned texts in single languages. Numéro de notice : A2018-214 Affiliation des auteurs : non IGN Thématique : TOPONYMIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12317 Date de publication en ligne : 29/01/2018 En ligne : https://doi.org/10.1111/tgis.12317 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90004
in Transactions in GIS > vol 22 n° 2 (April 2018) . - pp 394 - 408[article]Generating vague neighbourhoods through data mining of passive web data / Paul Brindley in International journal of geographical information science IJGIS, vol 32 n° 3-4 (March - April 2018)
[article]
Titre : Generating vague neighbourhoods through data mining of passive web data Type de document : Article/Communication Auteurs : Paul Brindley, Auteur ; James Goulding, Auteur ; M. L. Wilson, Auteur Année de publication : 2018 Article en page(s) : pp 498 - 523 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] adresse postale
[Termes IGN] base de données d'adresses
[Termes IGN] exploration de données géographiques
[Termes IGN] extraction automatique
[Termes IGN] limite indéterminée
[Termes IGN] recherche d'information géographique
[Termes IGN] structure sociale
[Termes IGN] voisinage (relation topologique)
[Termes IGN] zone urbaineRésumé : (Auteur) Neighbourhoods have been described as ‘the building blocks of public services society’. Their subjective nature, however, and the resulting difficulties in collecting data, means that in many countries there are no officially defined neighbourhoods either in terms of names or boundaries. This has implications not only for policy but also business and social decisions as a whole. With the absence of neighbourhood boundaries many studies resort to using standard administrative units as proxies. Such administrative geographies, however, often have a poor fit with those perceived by residents. Our approach detects these important social boundaries by automatically mining the Web en masse for passively declared neighbourhood data within postal addresses. Focusing on the United Kingdom (UK), this research demonstrates the feasibility of automated extraction of urban neighbourhood names and their subsequent mapping as vague entities. Importantly, and unlike previous work, our process does not require any neighbourhood names to be established a priori. Numéro de notice : A2018-043 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1400549 En ligne : https://doi.org/10.1080/13658816.2017.1400549 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89264
in International journal of geographical information science IJGIS > vol 32 n° 3-4 (March - April 2018) . - pp 498 - 523[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2018022 RAB Revue Centre de documentation En réserve L003 Disponible 079-2018021 RAB Revue Centre de documentation En réserve L003 Disponible Comparative study of visual saliency maps in the problem of classification of architectural images with Deep CNNs / Abraham Montoya Obeso (2018)
Titre : Comparative study of visual saliency maps in the problem of classification of architectural images with Deep CNNs Type de document : Article/Communication Auteurs : Abraham Montoya Obeso, Auteur ; Jenny Benois-Pineau, Auteur ; Kamel Guissous , Auteur ; Valérie Gouet-Brunet , Auteur ; Mireya S. García Vázquez, Auteur ; Alejandro A. Ramírez Acosta, Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2018 Projets : 2-Pas d'info accessible - article non ouvert / Conférence : IPTA 2018, 8th International Conference on Image Processing Theory, Tools and Applications 07/11/2018 10/11/2018 Xi'an Chine Proceedings IEEE Importance : pp 1 - 6 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] Bootstrap (statistique)
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] compréhension de l'image
[Termes IGN] exploration de données
[Termes IGN] recherche d'image basée sur le contenu
[Termes IGN] saillance
[Termes IGN] scène urbaineRésumé : (auteur) Incorporating Human Visual System (HVS) models into building of classifiers has become an intensively researched field in visual content mining. In the variety of models of HVS we are interested in so-called visual saliency maps. Contrarily to scan-paths they model instantaneous attention assigning the degree of interestingness/saliency for humans to each pixel in the image plane. In various tasks of visual content understanding, these maps proved to be efficient stressing contribution of the areas of interest in image plane to classifiers models. In previous works saliency layers have been introduced in Deep CNNs, showing that they allow reducing training time getting similar accuracy and loss values in optimal models. In case of large image collections efficient building of saliency maps is based on predictive models of visual attention. They are generally bottom-up and are not adapted to specific visual tasks. Unless they are built for specific content, such as "urban images"-targeted saliency maps we also compare in this paper. In present research we propose a "bootstrap" strategy of building visual saliency maps for particular tasks of visual data mining. A small collection of images relevant to the visual understanding problem is annotated with gaze fixations. Then the propagation to a large training dataset is ensured and compared with the classical GBVS model and a recent method of saliency for urban image content. The classification results within Deep CNN framework are promising compared to the purely automatic visual saliency prediction. Numéro de notice : C2018-097 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IPTA.2018.8608125 Date de publication en ligne : 14/01/2019 En ligne : https://doi.org/10.1109/IPTA.2018.8608125 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95885
Titre : Geospatial Analysis : a comprehensive guide to principles, techniques and software tools Type de document : Guide/Manuel Auteurs : Michael J. de Smith, Éditeur scientifique ; Michael F. Goodchild, Éditeur scientifique ; Paul A. Longley, Éditeur scientifique Mention d'édition : 6th edition Editeur : The Winchelsea Press Année de publication : 2018 Importance : 748 p. Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] exploration de données
[Termes IGN] géostatistiqueIndex. décimale : 37.20 Analyse spatiale et ses outils Résumé : (Editeur) [Introduction] In this Guide we address the full spectrum of spatial analysis and associated modeling techniques that are provided within currently available and widely used geographic information systems (GIS) and associated software. Collectively such techniques and tools are often now described as geospatial analysis, although we use the more common form, spatial analysis, in most of our discussions. The term ‘GIS’ is widely attributed to Roger Tomlinson and colleagues, who used it in 1963 to describe their activities in building a digital natural resource inventory system for Canada (Tomlinson 1967, 1970). The history of the field has been charted in an edited volume by Foresman (1998) containing contributions by many of its early protagonists. A timeline of many of the formative influences upon the field up to the year 2000 is available via: http://www.casa.ucl.ac.uk/gistimeline/; and is provided by Longley et al. (2010). Useful background information may be found at the GIS History Project website (NCGIA): http://www.ncgia.buffalo.edu/gishist/. Each of these sources makes the unassailable point that the success of GIS as an area of activity has fundamentally been driven by the success of its applications in solving real world problems. Many applications are illustrated in Longley et al. (Chapter 2, “A gallery of applications”). In a similar vein the web site for this Guide provides companion material focusing on applications. Amongst these are a series of sector‑specific case studies drawing on recent work in and around London (UK), together with a number of international case studies. In order to cover such a wide range of topics, this Guide has been divided into a number of main sections or chapters. These are then further subdivided, in part to identify distinct topics as closely as possible, facilitating the creation of a web site from the text of the Guide. Hyperlinks embedded within the document enable users of the web and PDF versions of this document to navigate around the Guide and to external sources of information, data, software, maps, and reading materials. [...] Note de contenu : 1. Introduction and terminology
2. Conceptual Frameworks for Spatial Analysis
3. Methodological Context
4. Building Blocks of Spatial Analysis
5. Data Exploration and Spatial Statistics
6. Surface and Field Analysis
7. Network and Location Analysis
8. Geocomputational methods and modeling
9. Afterword - Big Data and Geospatial AnalysisNuméro de notice : 22863 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Manuel En ligne : http://www.spatialanalysisonline.com/HTML/index.html Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89308
Titre : Machine learning and biometrics Type de document : Monographie Auteurs : Jucheng Yang, Éditeur scientifique ; Dong Sun Park, Éditeur scientifique ; Sook Yoon, Éditeur scientifique ; et al., Auteur Editeur : London [UK] : IntechOpen Année de publication : 2018 Importance : 146 p. Format : 17 x 24 cm ISBN/ISSN/EAN : 978-1-83881-556-1 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage profond
[Termes IGN] biométrie
[Termes IGN] exploration de données
[Termes IGN] interface homme-machine
[Termes IGN] reconnaissance facialeRésumé : (éditeur) We are entering the era of big data, and machine learning can be used to analyze this deluge of data automatically. Machine learning has been used to solve many interesting and often difficult real-world problems, and the biometrics is one of the leading applications of machine learning. This book introduces some new techniques on biometrics and machine learning, and new proposals of using machine learning techniques for biometrics as well. This book consists of two parts: ""Biometrics"" and ""Machine Learning for Biometrics."" Parts I and II contain four and three chapters, respectively. The book is reviewed by editors: Prof. Jucheng Yang, Prof. Dong Sun Park, Prof. Sook Yoon, Dr. Yarui Chen, and Dr. Chuanlei Zhang. Note de contenu : 1- Introductory chapter: machine learning and biometrics
2- Recognition of eye characteristics
3- A survey on soft biometrics for human identification
4- Face recognition with facial occlusion based on local cycle graph structure operator
5- Electrocardiogram recognization based on variational autoEncoder
6- A survey on methods of image processing and recognition for personal identification
7- A human body mathematical model biometric using golden ratio: A new algorithmNuméro de notice : 28505 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.71297 En ligne : https://doi.org/10.5772/intechopen.71297 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97028 Spatio-temporal grid mining applied to image classification and cellular automata analysis / Romain Deville (2018)PermalinkThe influence of domain expertise on visual overviews of spatiotemporal data / Susanne Bleisch in International journal of cartography, vol 3 n° 2 (December 2017)PermalinkA cloud-enabled automatic disaster analysis system of multi-sourced data streams: An example synthesizing social media, remote sensing and Wikipedia data / Qunying Huang in Computers, Environment and Urban Systems, vol 66 (November 2017)PermalinkSocial Distance metric: from coordinates to neighborhoods / Vagan Terziyan in International journal of geographical information science IJGIS, vol 31 n° 11-12 (November - December 2017)PermalinkAn iterative method for obtaining a mean 3D axis from a set of GNSS traces for use in positional controls / A. Mozas-Calvache in Survey review, vol 49 n° 355 (October 2017)PermalinkDiscovering non-compliant window co-occurrence patterns / Reem Y. Ali in Geoinformatica, vol 21 n° 4 (October - December 2017)PermalinkFacet segmentation-based line segment extraction for large-scale point clouds / Yangbin Lin in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)PermalinkAggregation-based information retrieval system for geospatial data catalogs / Javier Lacasta in International journal of geographical information science IJGIS, vol 31 n° 7-8 (July - August 2017)PermalinkPerSE : visual analytics for calendar related spatiotemporal periodicity detection and analysis / Brian Swedberg in Geoinformatica, vol 21 n° 3 (July - September 2017)PermalinkRobust point cloud classification based on multi-level semantic relationships for urban scenes / Qing Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 129 (July 2017)Permalink